Data and analytics have become important to navigate organizational strategic decisions. We have talked about data into new oil and propellers for digital transformation for some time now. But the data is not more useful - it's just numbers, numbers, and some graphics, which don't tell anything.
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For organizations can develop, implement, and disseminate the strategies that are informed properly and carefully designed, they need insight and not just a simple amount. The numbers need to tell the story of what has been, and what might happen.
The insights offered by the number of business intelligence functions, which organizations have been used for years to determine their next actions. But traditionally, business intelligence involves stirring extensive manual data to obtain valuable information. At present, with a quick increase in the scale and volume of data generation elements, from IoT devices to smart phones, traditional business intelligence requires fast pivots to more efficient labor and less efficient processes. This is where Augmented Analytics enters.
This also adds to expert data scientists and residents by automating many aspects Data Science, Machine Learning, and Development of AI Models, Management and Distribution. "
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Let's understand how augmented analytics, with the help of Smart Next-gen technology such as artificial intelligence, machine learning, and natural language processing, changing the face of business intelligence-driven by data.
Limited traditional business intelligence
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The process of traditional business intelligence consists of two parts - data collection and data analysis.
This process requires highly skilled professional work with rich technical expertise to collect data from various, different sources, and then apply analytic schemes to information collected to produce business-critical information. The main disadvantage of this process is:
First, there are exact shortcomings that have desirable expertise and expertise to efficiently carry out mining & data analysis.
Because of the whole manual process, it is very susceptible to errors. The wrong results can cause expensive and embarrassable errors that can cost organizations, customers, and brand values.
Manual data analysis becomes very time consuming expanding the duration between initiation and completion of the process. When the analysis is complete, the data becomes obsolete and insight makes it redundant.
Need for a smarter business intelligence solution
Organizations are able to spend several months on certain data mining set in the pre-digital era. But now, when around 2MB of data is being produced every second for everyone on earth, business intelligence solutions need to be faster and smarter. Modern business intelligence solutions needed:
Speed up data collection from different sources such as Cloud Storage, IoT sensors, in-house databases, and software applications in real time
Verify and validate authenticity, relevance, completeness, and truth of the data collected
Conduct mining and analytics in the data collected to produce desired information and insights
Offering insights that are derived in a very friendly and readable format like a dashboard
Ai in Analytics.
AI is what basically adds to modern business intelligence solutions. But its role does not limit the automation and acceleration of traditional manual processes.
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